A Dynamic Network Model of the Color Visual Pathways for Attentive Recognition

Abstract

A neural network architecture for the segmentation and recognition of colored and textured visual stimuli is presented. The architecture is based on the Boundary Contour System and Feature Contour System (BCS/FCS) of S. Grossberg and E. Mingolla. The architecture proposes a biologically-inspired mechanism for color processing based on antagonist interactions. It suggests how information from different modalities (i.e. color or texture) can be fused together to form a coherent segmentation of the visual scene. It identifies two stages of visual pattern recognition, namely, a global preattentive recognition of the visual scene followed by a local attentive recognition within a particular visual context. The global and local classification and recognition of visual stimuli use ART-type models of G. Carpenter and S. Grossberg for pattern learning and recognition based on color and texture. One example is presented corresponding to an figure-figure separation task. The architecture provides a mechanism for segmentation, categorization and recognition of images from different classes based on self-organizing principles of perception and pattern recognition.